Joao Gari da Silva Fonseca Junior

On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation

The objective of this study is to propose a method to calculate prediction intervals for oneday-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.

Power system operation with battery charge/discharge scheduling based on interval analysis

AbstractThe use of photovoltaic (PV) generation forecasts in economic load dispatching control, which includes the unit commitment of conventional power plants, is essential to ensure the economic performance and the reliability of power systems. In the previous study, we developed a day-ahead charge and discharge scheduling method of battery energy storage systems based on interval analysis using prediction intervals of a PV generation forecast; this interval forecast considers forecast errors and gives not only the forecasted output but also the possible range of the actual output with a certain confidence. In this study, we evaluate the proposed scheduling method by numerical simulations in terms of the power system supply and demand operation.